Wet'suwet'en Women Leading the Defense of Rivers and Water From Abuses Committed in Connection with Megaprojects. The Persistent Legacies of the Past in Canada
Bibliographic record
Abstract
For over a decade, Wet’suwet’en women have been leading the defense of the Yintah (their ancestral territory) against the construction of megaprojects, including the largest private investment project in Canada, the Coastal GasLink (CGL) pipeline. The pipeline crosses over the north of the British Columbia province, from the east to the Pacific coast, including the Wet’suwet’en Yintah and Wedzin Kwa, a sacred and fundamental river for this Indigenous people. The Wet’suwet’en women-led mobilization, including their Hereditary Chiefs, is one of Canada’s most visible and supported. They have consistently argued that they have never granted consent to CGL to work in their territory. They have insistently called on the Canadian federal and provincial authorities and the corporations involved to stop the project in the Yintah, also raising the issue to the attention of an international audience. Yet, at the end of 2022, CGL started drilling under Wedzin Kwa. What explains that the Wet’suwet’en women-led mobilization has not impacted corporate behavior? Drawing on the “braided action” theoretical framework, which responds to a similar question in the context of Latin America, this chapter argues that one key aspect of a possible explanation is the legacies of a colonial past that persist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".